Changes of water state and gel characteristics of Hairtail (<scp><i>Trichiurus lepturus</i></scp>) surimi during thermal processing
Bibliographic record
Abstract
relaxation time of water and gel strength increased from 47.01 to 78.97% and from 64.23 to 51.52 ms, respectively, and whiteness decreased from 63.87 to 55.22 during the entire thermal processing. Meanwhile, the texture properties including hardness, gumminess, and chewiness declined from 402.42 to 130.41 g, from 294.39 to103.70 g, and from 233.68 to 43.60 g, respectively, during the first step, and then increased markedly during the second step from 130.41 to 2,301.87 g, from 103.70 to 1,250.99 g, and from 43.60 to 978.51 g, respectively. Furthermore, the WHC and textural profile had positive correlation, and changes in protein secondary structure were interesting, with the α-helices decreasing significantly from 26.40 to 14.12%, while the β-sheet and the random coil structure increasing significantly from 36.28 to 44.03%, and from 10.89 to 14.31%, respectively, and β-turn structure increasing form 26.44 to 27.98% during the first step and then declining markedly during the second step, moreover β-sheet had a fine positive correlation with WHC hardness and chewiness. Overall, dense, porous and compact three-dimensional network gel structure gradually formed. In a word, during thermal processing. WHC of Hairtail surimi increased, and protein secondary structure of protein became orderly, and a fine, dense gel formed during thermal processing. Water is considered as the highest and most important chemical constituent in surimi products. During surimi gelation, water molecules exist as bulk water and motionally restricted water on the protein surface. In order to gain more insights into the surimi heating-induced gelation processing, and improve the surimi gel properties, and give same advice to manufacturing enterprise, this work was conducted to study the structural changes of protein and water state during surimi gelation processing and performed along with the monitoring of the texture, WHC and other physical characteristics of surimi gel, as well as the microstructure of surimi gel.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".